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This paper considers an artificial noise (AN)-aided transmit design formulti-user MIMO systems with integrated services. Specifically, two sorts ofservice messages are combined and served simultaneously: one multicast messageintended for all receivers and one confidential message intended for only onereceiver and required to be perfectly secure from other unauthorized receivers.Our interest lies in the joint design of input covariances of the multicastmessage, confidential message and artificial noise (AN), such that theachievable secrecy rate and multicast rate are simultaneously maximized. Thisproblem is identified as a secrecy rate region maximization (SRRM) problem inthe context of physical-layer service integration. Since this bi-objectiveoptimization problem is inherently complex to solve, we put forward twodifferent scalarization methods to convert it into a scalar optimizationproblem. First, we propose to prefix the multicast rate as a constant, andaccordingly, the primal biobjective problem is converted into a secrecy ratemaximization (SRM) problem with quality of multicast service (QoMS) constraint.By varying the constant, we can obtain different Pareto optimal points. Theresulting SRM problem can be iteratively solved via a provably convergentdifference-of-concave (DC) algorithm. In the second method, we aim to maximizethe weighted sum of the secrecy rate and the multicast rate. Through varyingthe weighted vector, one can also obtain different Pareto optimal points. Weshow that this weighted sum rate maximization (WSRM) problem can be recast intoa primal decomposable form, which is amenable to alternating optimization (AO).Then we compare these two scalarization methods in terms of their overallperformance and computational complexity via theoretical analysis as well asnumerical simulation, based on which new insights can be drawn.

Artificial Noise-Aided Biobjective Transmitter Optimization for Service Integration in Multi-User MIMO Gaussian Broadcast Channel